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Title:Cross-Hole GPR for Soil Moisture Estimation Using Deep Learning
Authors:ID Pongrac, Blaž (Author)
ID Gleich, Dušan (Author)
ID Malajner, Marko (Author)
ID Sarjaš, Andrej (Author)
Files:.pdf Pongrac-2023-Cross-Hole_GPR_for_Soil_Moisture.pdf (3,22 MB)
MD5: A343C58E748F8AA807F3EF13F6EA6742
 
URL https://www.mdpi.com/2072-4292/15/9/2397
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:This paper presents the design of a high-voltage pulse-based radar and a supervised data processing method for soil moisture estimation. The goal of this research was to design a pulse-based radar to detect changes in soil moisture using a cross-hole approach. The pulse-based radar with three transmitting antennas was placed into a 12 m deep hole, and a receiver with three receive antennas was placed into a different hole separated by 100 m from the transmitter. The pulse generator was based on a Marx generator with an LC filter, and for the receiver, the high-frequency data acquisition card was used, which can acquire signals using 3 Gigabytes per second. Used borehole antennas were designed to operate in the wide frequency band to ensure signal propagation through the soil. A deep regression convolutional network is proposed in this paper to estimate volumetric soil moisture using time-sampled signals. A regression convolutional network is extended to three dimensions to model changes in wave propagation between the transmitted and received signals. The training dataset was acquired during the period of 73 days of acquisition between two boreholes separated by 100 m. The soil moisture measurements were acquired at three points 25 m apart to provide ground truth data. Additionally, water was poured into several specially prepared boreholes between transmitter and receiver antennas to acquire additional dataset for training, validation, and testing of convolutional neural networks. Experimental results showed that the proposed system is able to detect changes in the volumetric soil moisture using Tx and Rx antennas.
Keywords:ground penetrating radar, cross-hole, L-band, deep learning, convolutional neural network, soil moisture estimation
Publication status:Published
Publication version:Version of Record
Submitted for review:13.02.2023
Article acceptance date:26.04.2023
Publication date:04.05.2023
Publisher:MDPI
Year of publishing:2023
Number of pages:Str. 1-17
Numbering:Letn. 15, Št. 9, št. članka 2397
PID:20.500.12556/DKUM-87960 New window
UDC:681.5
ISSN on article:2072-4292
COBISS.SI-ID:153415939 New window
DOI:10.3390/rs15092397 New window
Publication date in DKUM:03.04.2024
Views:448
Downloads:26
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
PONGRAC, Blaž, GLEICH, Dušan, MALAJNER, Marko and SARJAŠ, Andrej, 2023, Cross-Hole GPR for Soil Moisture Estimation Using Deep Learning. Remote sensing [online]. 2023. Vol. 15, no. Št. 9,  članka 2397, p. 1–17. [Accessed 27 March 2025]. DOI 10.3390/rs15092397. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=87960
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Record is a part of a journal

Title:Remote sensing
Shortened title:Remote sens.
Publisher:MDPI
ISSN:2072-4292
COBISS.SI-ID:32345133 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0065
Name:Telematika

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:04.05.2023

Secondary language

Language:Slovenian
Keywords:radarji, globoko učenje, konvolucijske nevronske mreže, ocena vlažnosti tal


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